Job Offers

The Intelligent Systems Group at Paderborn University is seeking for highly qualified doctoral or postdoctoral researchers interested in machine learning. Candidates are expected to conduct research within a project funded by the German Research Foundation. The contract is for three years, and the payment is determined according to the competitive German TVL E-13 scheme (depending on the candidate's experience and qualifications). Within the project, there is a possibility for a cooperation with Robert Busa-Fekete from Yahoo! Research, New York.

P O S I T I O N  R E Q U I R E M E N T S

Ph.D. position applicants need to combine excellent skills in mathematics, statistics, and computer science. A successful postdoc applicant should have a strong background in machine learning with a corresponding track record of research publications, including top-tier conferences (e.g., ICML, NIPS, AISTATS, IJCAI, AAAI) and journals (e.g., JMLR, MLJ). Ideally, an applicant has experience on topics relevant for the project (see below).

H O W  T O  A P P L Y

Ph.D. applicants should provide a research statement, their CV, degrees including grade-sheets, and two references who are willing to write a recommendation letter. Postdoc applicants should additionally provide their top three publications. Please submit complete applications, preferably combined in a single PDF file, to Prof. Eyke Hüllermeier (eyke@upb.de). Please state the reference number 2847 in the subject. There is no fixed deadline, but the positions will be filled as soon as possible.

T H E  P R O J E C T

In machine learning, the notion of multi-armed bandit (MAB) refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. Combining theoretical challenge with practical usefulness, MABs have received considerable attention in machine learning research in the recent past. This project is devoted to a variant of standard MABs that is referred to as the dueling bandit or preference-based multi-armed bandit (PB-MAP) problem. Instead of learning from stochastic feedback in the form of real-valued rewards for the choice of single alternatives, a PB-MAB agent is allowed to compare pairs of alternatives in a qualitative manner. The goal of this project is to address several open research questions related to the PB-MAB setting, and to study variants and extensions of this setting.

M O R E  I N F O R M A T I O N

The homepage of the Intelligent Systems group can be found here:

https://www.cs.uni-paderborn.de/fachgebiete/intelligente-systeme.html